Assessing The Quality Factors Found in In-Line Documentation Written in Natural Language: The JavadocMiner
Bibliographic record
Abstract
An important software engineering artifact used by \ndevelopers and maintainers to assist in software comprehension and maintenance is source code documentation. It provides the insight needed by software engineers when performing a task, and therefore ensuring the quality of documentation is extremely important. In-line documentation is at the forefront of explaining a programmer's original intentions for a given implementation. Since this documentation is written in informal natural language, ensuring its quality needs to be performed manually. In this works, we present an effective and automated approach for assessing the quality of in-line documentation using a set of heuristics, targeting both the quality of language and consistency between source code and its comments. Our evaluation is made up of three parts: We first apply the JavadocMiner tool to the different modules of two \nopen source applications (ArgoUML and Eclipse) in order to \nautomatically assess their intrinsic comment quality. In the \nsecond part of our evaluation, we correlate the results \nreturned by the analysis with bug defects reported for the \nindividual modules in order to examine connections between \nnatural language documentation and source code quality. And \nfinally, we compare the comment quality results generated \nusing our JavadocMiner with the quality assessments performed manually by undergraduate and graduate computer science students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".